Correlation of Model and Human Observer Performance On a Lesion Shape Discrimination Task Using Realistic and Repeated CT Scans

Y Zhang*, S Leng, L Yu, C McCollough, Mayo Clinic, Rochester, MN

SU-F-500-8 Sunday 4:00PM - 6:00PM Room: 500 Ballroom

Purpose: To investigate how well a channelized Hotelling observer (CHO) can predict the human observer performance in a lesion shape discrimination task using realistic and repeated CT scans for both filtered backprojection (FBP) and iterative reconstruction (IR).

Methods: Eight lesion mimicking rods (2 contrasts, 2 sizes, 2 shapes) were inserted into a 35 by 26 cm water phantom and scanned 100 times on a 128-slice CT scanner (Definition Flash, Siemens Healthcare) with 120 kV at 120, 180, 240, 360 and 480 quality reference mass (careDose4D). CT images were reconstructed using a FBP and an IR method. A region of interest centered in the rod was extracted to construct the 2-alternative forced choice (2AFC) study with hexagon and circle rod images put side-by-side in randomized order. Total 4000 2AFC trials were read by three medical physicists and the CHO with Gabor filter (5 passbands, 5 orientations, 2 phases). An edge-emphasis binary mask was applied at the CHO input end. Percent correct was calculated and non-parametric statistical analysis was performed to compare the performance between human and model observers, and between FBP and IR.

Results: The Spearman rank correlation coefficient was 0.879 for FBP and 0.916 for IR (P<0.01). Bland-Altman plots showed excellent agreement between human and model observers for all lesions sizes and contrasts with a mean difference of 0.5%±3.6% for FBP and 0.2%±3.1% for IR. IR significantly increased the human and model observers performances compared to FBP (Wilcoxon signed rank test, P<0.01).

Conclusion: This study demonstrates a high correlation between human and model observers in a discrimination task using realistic CT images. Masking the image input to the CHO by an edge emphasis mask mimics the higher weighting applied towards the edge when humans performed the shape discrimination task. IR significantly increased both human and model performance compared to FBP.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by NIH grant R01 EB071095 from the National Institute of Biomedical Imaging and Bioengineering.